1. Field of the Invention
The present invention relates to a technique for measuring three-dimensional information.
2. Description of Related Art
A technique for constructing a three-dimensional model from three-dimensional point cloud position data of an object is known. In the three-dimensional point cloud position data, a two-dimensional image is linked with three-dimensional coordinates. That is, in the three-dimensional point cloud position data, data of a two-dimensional image of an object, plural measured points (point cloud) that are matched with the two-dimensional image, and positions (three-dimensional coordinates) of the measured points in a three-dimensional space, are associated with each other. According to the three-dimensional point cloud position data, a three-dimensional model of a reproduced outer shape of the object is obtained by using a set of points. In addition, since three-dimensional coordinates of each point are obtained, the relative position of each point in the three-dimensional space is understood. Therefore, a screen-displayed image of a three-dimensional model can be rotated, and the image can be switched to an image that is viewed from a different position.
For example, in the invention disclosed in Japanese Unexamined Patent Application Publication (Translation of PCT Application) No. 2000-509150, a scanning laser device scans a three-dimensional object and generates point clouds. The point clouds are divided into the group of edge points and the group of non-edge points, based on changes in depths and normal lines of the scanned points. Each group is fitted to geometric original drawings, and the fitted geometric original drawings are extended and are crossed, whereby a three-dimensional model is constructed.
In the invention disclosed in Japanese Unexamined Patent Application Laid-open No. 2004-272459, segments (triangular polygons) are formed from three-dimensional point cloud position data, and edges and planes are extracted based on continuity, directions of normal lines, or distance, of adjacent polygons. Then, the three-dimensional point cloud position data of each segment is converted into a flat plane equation or a curved plane equation by the least-squares method and is grouped by planarity and curvature, whereby a three-dimensional model is constructed.
In the invention disclosed in Japanese Unexamined Patent Application Laid-open No. 2005-024370, two-dimensional rectangular areas are set for three-dimensional point cloud position data, and synthesized normal vectors of measured points in the rectangular areas are obtained. All of the measured points in the rectangular area are rotationally shifted so that the synthesized normal vector corresponds to a z-axis direction. Standard deviation σ of z value of each of the measured points in the rectangular area is calculated. Then, when the standard deviation σ exceeds a predetermined value, the measured point corresponding to the center point in the rectangular area is processed as noise.
In a case of obtaining three-dimensional point cloud position data by using a laser scanner, three-dimensional point cloud position data of a part behind an object cannot be obtained because the part is in the shadow of the object when seen from the laser scanner. Such generation of a shadow part is called “occlusion”. The three-dimensional point cloud position data of the shadow part, which could not be obtained, can be obtained by changing the position to a position from which laser light can be emitted to the shadow part and by scanning again.
In this case, in order to solve the occlusion by this method, a processing of position adjustment is required so as to use from several tens of thousands to tens of millions of three-dimensional point cloud position data obtained at each of two positions. This processing is complicated and takes a long time to complete.
In order to adjust the positions, plural targets may be preliminarily attached to the object so as to clarify matching points between the two sets of three-dimensional point cloud position data. Then, the positions may be adjusted between the two sets of three-dimensional point cloud position data, based on the targets. Since targets must be attached on the object in this method, for example, when a tall structure or the like is to be measured, work at high places is required, and stages for the work at high places are difficult to set. Thus, there may be cases in which targets are not easily attached.